Title: A three-stage approach using deep learning for automated vehicle smart parking with license plate recognition

Authors: Shaunak Gupta; Pushkar Garg; Abhinav Aggarwal; Gaurav Goyal; Kanu Goel

Addresses: Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, 160012, India ' Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, 160012, India ' Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, 160012, India ' Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, 160012, India ' Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, 160012, India

Abstract: Urbanisation and increasing vehicular density have amplified challenges in conventional parking systems, necessitating innovative solutions for effective parking management. Common challenges include the absence of real-time information on available parking slots, limited data analysis tools for space utilisation, reliance on manual processes, and the inadequate incorporation of automation at entry and exit of vehicles. The proposed smart parking approach endeavours to mitigate these challenges by integrating advanced technologies to enable efficient parking slot detection, comprehensive data analysis, and seamless automation. It integrates automation at entry and exit points, streamlining the parking process. Leveraging advanced computer vision and sensor technologies, the system provides real-time identification of empty parking slots, enhancing overall space utilisation. The system also incorporates accident tracking mechanisms to enhance safety within parking facilities. The research paper also presents a novel accident detection model attaining a commendable accuracy of 94.5%.

Keywords: artificial intelligence; smart parking; deep learning; automated number plate recognition; ANPR; pre-trained models; convolutional neural network; CNN.

DOI: 10.1504/IJAHUC.2025.145200

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.48 No.4, pp.212 - 223

Received: 01 Mar 2024
Accepted: 18 Sep 2024
Published online: 25 Mar 2025 *

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